Data Architect

Job Summary

The Data Architect is responsible for designing, developing, and governing the enterprise data architecture that enables reliable, secure, scalable, and accessible data across the organization. This role establishes the frameworks, standards, models, and integration patterns that define how data is collected, structured, stored, integrated, governed, and consumed across business applications and analytics platforms.

The Data Architect serves as a strategic and technical partner to business leaders, IT, application teams, data and analytics teams, and external partners. The role is particularly important in an environment with multiple business units, brands, locations, ERP platforms, and acquired or integrated businesses.

The successful candidate will balance long-term enterprise architecture with practical business needs, helping the organization improve data quality, simplify its technology landscape, strengthen governance, and establish a trusted foundation for reporting, analytics, automation, and artificial intelligence.

Duties and Responsibilities

Enterprise Data Architecture

• Develop and maintain the enterprise data architecture strategy, standards, principles, and roadmaps aligned with business and technology objectives.
• Design scalable data architectures across cloud, on-premise, and hybrid environments supporting structured and unstructured data.
• Define current and future-state architectures, reduce complexity and technical debt, and establish patterns for data ingestion, storage, integration, transformation, consumption, and retention.
• Evaluate emerging technologies and ensure architecture decisions enable future capabilities in analytics, AI, machine learning, and automation.

Data Modeling & Design

• Develop and govern conceptual, logical, and physical data models supporting enterprise applications, reporting, analytics, and integrations.
• Establish enterprise data standards, definitions, canonical models, metadata, lineage, retention, and lifecycle management practices.
• Partner with business and technical teams to identify authoritative data sources and ensure models support both operational and analytical needs.

ERP & Enterprise Systems Integration

• Provide architecture leadership for SAP S/4HANA and other enterprise platforms, including finance, HR, CRM, manufacturing, supply chain, and e-commerce systems.
• Define integration standards and data exchange frameworks across applications, acquired businesses, legacy systems, and external partners.
• Support implementations, migrations, conversions, and transformation initiatives while ensuring cross-functional business processes are effectively integrated.

Data Governance & Quality

• Define data ownership, stewardship, and accountability while implementing controls that ensure data quality, consistency, accuracy, completeness, and timeliness.
• Develop processes to identify and resolve systemic data issues and maintain enterprise metadata, lineage, data catalogs, and business glossaries.

Data Integration & Analytics Enablement

• Define architecture standards for APIs, data pipelines, ETL/ELT processes, warehouses, lakes, and analytics platforms.
• Collaborate with reporting and analytics teams to ensure data is structured, governed, and accessible for enterprise reporting and self-service analytics.
• Promote reusable integration patterns and reduce redundant data repositories and point-to-point integrations.

AI & Advanced Analytics Readiness

• Establish data architecture requirements that support AI, machine learning, predictive analytics, and generative AI initiatives.
• Assess data readiness for business use cases and ensure AI solutions leverage secure, governed, accurate, and traceable enterprise data from multiple sources.

Security, Privacy & Risk

• Collaborate with cybersecurity, legal, privacy, and compliance teams to incorporate security and regulatory requirements into data architecture.
• Define standards for data classification, access, encryption, retention, and disposal, ensuring sensitive information is protected throughout its lifecycle.

Business Partnership & Architecture Governance

• Translate complex technical concepts into clear business recommendations, risks, and opportunities.
• Facilitate architecture governance, standards reviews, and decision-making processes across business and technology teams.
• Partner with project teams throughout solution delivery and guide stakeholders toward decisions that support long-term enterprise data strategy.

Qualification Requirements

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, or a related field.
• 8+ years of progressive experience in data architecture, data engineering, enterprise architecture, application architecture, or related disciplines.
• 3+ years of experience performing enterprise or solution-level data architecture responsibilities.
• Demonstrated experience designing data architectures across complex enterprise technology environments.
• Strong knowledge of relational and dimensional data modeling, data integration, master data, metadata, data lineage, and data governance to enable enterprise level Artificial Intelligence capabilities.
• Experience with modern cloud data platforms, data warehouses, data lakes/lakehouses, ETL/ELT, APIs, and integration technologies.
• Experience with platforms such as Microsoft Azure, AWS, Snowflake, Databricks, Microsoft Fabric, or comparable technologies.
• Experience with Power BI or similar enterprise analytics platforms.
• Demonstrated ability to translate business requirements into scalable technical architecture.
• Experience working across multiple functions, systems, and business processes.

Preferred Qualifications

• Experience with SAP S/4HANA and SAP data structures, integration, or master-data environments.
• Experience in manufacturing, consumer products, retail, supply chain, distribution, or similarly complex operating environments.
• Experience integrating data following acquisitions or across multiple business entities and technology environments.
• Experience establishing or improving enterprise data governance and master-data-management programs.
• Familiarity with AI/ML data architectures and data-readiness requirements.
• Relevant certifications in cloud architecture, data management, SAP, TOGAF, DAMA/CDMP, or related disciplines.

Knowledge, Skills and Abilities

Enterprise Thinking: Understands the interdependencies among business processes, applications, data, and technology and makes decisions for the enterprise rather than optimizing a single function.

Architecture & Technical Depth: Demonstrates strong expertise in data modeling, integration, platforms, governance, and modern data architecture.

Business Acumen: Understands how data supports business processes and can connect architecture decisions to operational and financial outcomes.

Strategic Thinking: Balances immediate project requirements with long-term architectural objectives.

Influence & Collaboration: Builds alignment across technical and non-technical stakeholders and effectively challenges decisions that introduce unnecessary complexity or risk.

Problem Solving: Diagnoses complex data problems, identifies root causes, evaluates alternatives, and recommends sustainable solutions.

Communication: Communicates technical concepts clearly to executives, business leaders, project teams, and technical professionals.

Execution: Converts architecture strategy into practical standards, designs, roadmaps, and implementation requirements.

Physical Requirements

Normal office environment